library(pitchRx)
# returns a list of related tables (see diagram below)
dat <- scrape(start = "2008-01-01", end = Sys.Date())

db <- dplyr::src_sqlite("pitchRx.sqlite3", create = TRUE)
pitchRx::scrape(start = "2008-01-01", end = Sys.Date(), connect = db$con)

• Any database connection should work!
• Writes data in streaming chunks to avoid exhausting memory.
• Keeping your database up-to-date is also easy!

update_db(db$con)

• Add other data!
  • openWAR estimates (open source calculation of Wins Above Replacement)
  • Exit Velocity and Distance of Batted Balls

source

Player/date info recorded on the at-bat level.

library(dplyr)
atbats <- tbl(db, 'atbat') %>%
  filter(pitcher_name == 'Yu Darvish', batter_name == 'Albert Pujols', 
         date == '2013_04_24')

• Now, obtain PITCHf/x data for these at-bats.

tbl(db, 'pitch') %>%
  inner_join(atbats, by = c('num', 'gameday_link')) %>%
  collect() %>% pitchRx::animateFX()

• How do we test for a significant overall difference between scenario A and B?
• Possible solution: Visual Inference